U.S. patent number 10,383,786 [Application Number 15/844,981] was granted by the patent office on 2019-08-20 for utilizing a human compound eye using an internet of things ("hcei") for obstacle protection of a user.
This patent grant is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The grantee listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Faried Abrahams, Amol Dhondse, Maharaj Mukherjee, Anand Pikle, Gandhi Sivakumar.
United States Patent |
10,383,786 |
Pikle , et al. |
August 20, 2019 |
Utilizing a human compound eye using an internet of things ("HCEI")
for obstacle protection of a user
Abstract
Embodiments for utilizing a human compound eye using internet of
things ("HCEI") for obstacle protection of a user by a processor.
One or more objects may be determined within an obstacle threshold
distance in relation to a user according to data captured from one
or more internet of things (IoT) devices associated with a wound
dressing, a mobility assistance device, or a combination thereof.
The user may be alerted of the one or more objects within the
obstacle threshold distance.
Inventors: |
Pikle; Anand (Pune,
IN), Dhondse; Amol (Pune, IN), Sivakumar;
Gandhi (Bentleigh, AU), Mukherjee; Maharaj
(Poughkeepsie, NY), Abrahams; Faried (Laytonsville, MD) |
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION (Armonk, NY)
|
Family
ID: |
66815421 |
Appl.
No.: |
15/844,981 |
Filed: |
December 18, 2017 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20190183720 A1 |
Jun 20, 2019 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61H
3/061 (20130101); G09B 21/001 (20130101); G09B
21/006 (20130101); G01S 15/87 (20130101); H04N
5/2252 (20130101); A61H 2201/5079 (20130101); H04N
5/2257 (20130101); G01C 21/206 (20130101) |
Current International
Class: |
A61H
3/06 (20060101); G01S 15/87 (20060101); G09B
21/00 (20060101); G01C 21/20 (20060101); H04N
5/225 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Bourbakis, "Sensing Surrounding 3-D Space for Navigation of the
Blind," IEEE Engineering in Medicine and Biology Magazine,
Jan./Feb. 2008 (7 pages). cited by applicant.
|
Primary Examiner: Miller; Brian E
Attorney, Agent or Firm: Griffiths & Seaton PLLC
Claims
The invention claimed is:
1. A method for enhancing communications for a user by a processor
comprised within a computer in communication with one or more
Internet of Things (IoT) devices, comprising: determining one or
more objects within an obstacle threshold distance in relation to a
user according to data captured from the one or more IoT devices
associated with a wound dressing, a mobility assistance device, or
a combination thereof; wherein the determining further comprises
predicting movement of the one or more objects using data obtained
from an obstacle threshold distance map or data obtained in
real-time from one or more IoT devices associated with the user;
and alerting the user of the one or more objects within the
obstacle threshold distance.
2. The method of claim 1, further including: determining a
real-time distance of the one or more objects from the user; and
storing the distance of the one or more objects from the user in
the obstacle threshold distance map so as to determine the one or
more objects within the obstacle threshold distance.
3. The method of claim 1, further including determining a speed, a
velocity, a distance, or a combination thereof of the one or more
objects in relation to the user, wherein the determined speed, the
velocity, the distance, or a combination thereof are stored in the
obstacle threshold distance map.
4. The method of claim 1, further including identifying the one or
more objects via the one or more IoT devices embedded into one or
more locations of the wound dressing, the mobility assistance
device, or a combination thereof, wherein the one or more IoT
devices include one or more types of sensors, media devices, audio
devices, or a combination thereof.
5. The method of claim 1, further including initializing a machine
learning mechanism to learn, set, or modify the obstacle threshold
distance for each of the one or more objects, parameters in the
obstacle threshold distance map, activities of daily living (ADL),
movement patterns of the user, or a combination thereof.
6. The method of claim 1, further including providing one or more
alerts, suggestions, communications, or a combination thereof via a
graphical user interface to the user so as to modify mobility or a
course of action to avoid the one or more objects within the
obstacle threshold distance.
7. A system for enhancing communications, comprising: one or more
computers with executable instructions that when executed cause the
system to: determine one or more objects within an obstacle
threshold distance in relation to a user according to data captured
from one or more Internet of Things (IoT) devices associated with a
wound dressing, a mobility assistance device, or a combination
thereof; wherein the determining further comprises predicting
movement of the one or more objects using data obtained from an
obstacle threshold distance map or data obtained in real-time from
one or more IoT devices associated with the user; and alert the
user of the one or more objects within the obstacle threshold
distance.
8. The system of claim 7, wherein the executable instructions:
determine a real-time distance of the one or more objects from the
user; and store the distance of the one or more objects from the
user in the obstacle threshold distance map so as to determine the
one or more objects within the obstacle threshold distance.
9. The system of claim 7, wherein the executable instructions
determine a speed, a velocity, a distance, or a combination thereof
of the one or more objects in relation to the user, wherein the
determined speed, the velocity, the distance, or a combination
thereof are stored in the obstacle threshold distance map.
10. The system of claim 7, wherein the executable instructions
identify the one or more objects via the one or more IoT devices
embedded into one or more locations of the wound dressing, the
mobility assistance device, or a combination thereof, wherein the
one or more IoT devices include one or more types of sensors, media
devices, audio devices, or a combination thereof.
11. The system of claim 7, wherein the executable instructions
initialize a machine learning mechanism to learn, set, or modify
the obstacle threshold distance for each of the one or more
objects, parameters in the obstacle threshold distance map,
activities of daily living (ADL), movement patterns of the user, or
a combination thereof.
12. The system of claim 7, wherein the executable instructions
provide one or more alerts, suggestions, communications, or a
combination thereof via a graphical user interface to the user so
as to modify mobility or a course of action to avoid the one or
more objects within the obstacle threshold distance.
13. A computer program product for enhancing communications for a
user by a processor comprised within a computer in communication
with one or more Internet of Things (IoT) devices, the computer
program product comprising a non-transitory computer-readable
storage medium having computer-readable program code portions
stored therein, the computer-readable program code portions
comprising: an executable portion that determines one or more
objects within an obstacle threshold distance in relation to a user
according to data captured from the one or more IoT devices
associated with a wound dressing, a mobility assistance device, or
a combination thereof; wherein the determining further comprises
predicting movement of the one or more objects using data obtained
from an obstacle threshold distance map or data obtained in
real-time from one or more IoT devices associated with the user;
and an executable portion that alerts the user of the one or more
objects within the obstacle threshold distance.
14. The computer program product of claim 13, further including an
executable portion that: determines a real-time distance of the one
or more objects from the user; and stores the distance of the one
or more objects from the user in the obstacle threshold distance
map so as to determine the one or more objects within the obstacle
threshold distance.
15. The computer program product of claim 13, further including an
executable portion that determines a speed, a velocity, a distance,
or a combination thereof of the one or more objects in relation to
the user, wherein the determined speed, the velocity, the distance,
or a combination thereof are stored in the obstacle threshold
distance map.
16. The computer program product of claim 13, further including an
executable portion that identifies the one or more objects via the
one or more IoT devices embedded into one or more locations of the
wound dressing, the mobility assistance device, or a combination
thereof, wherein the one or more IoT devices include one or more
types of sensors, media devices, audio devices, or a combination
thereof.
17. The computer program product of claim 13, further including an
executable portion that initializes a machine learning mechanism to
learn, set, or modify the obstacle threshold distance for each of
the one or more objects, parameters in the obstacle threshold
distance map, activities of daily living (ADL), movement patterns
of the user, or a combination thereof.
18. The computer program product of claim 13, further including an
executable portion that provides one or more alerts, suggestions,
communications, or a combination thereof via a graphical user
interface to the user so as to modify mobility or a course of
action to avoid the one or more objects within the obstacle
threshold distance.
Description
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention relates in general to computing systems, and
more particularly to, various embodiments for utilizing a human
compound eye using an internet of things ("HCEI") for protecting
users from objects by a processor.
Description of the Related Art
In today's society, many persons suffer various limitations
relating to vision and often times requiring vision correction or
augmentation. Moreover, many persons may also require assistance
from other persons to assist with identifying objects, hazards, or
other conditions potentially exposing a person with vision
impairment from causing harm to themselves while avoiding these
unseen dangers. The advent of computers and networking technologies
have made possible assisting persons suffering various physical
limitations.
SUMMARY OF THE INVENTION
Various embodiments for utilizing a human compound eye using an
internet of things ("HCEI") for obstacle protection of a user by a
processor are provided. In one embodiment, by way of example only,
a method for protecting a user from one or more objects using an
HCEI device, again by a processor, is provided. One or more objects
may be determined within an obstacle threshold distance in relation
to a user according to data captured from one or more internet of
things (IoT) devices associated with a wound dressing, a mobility
assistance device, or a combination thereof. The user may be
alerted of the one or more objects within the obstacle threshold
distance.
BRIEF DESCRIPTION OF THE DRAWINGS
In order that the advantages of the invention will be readily
understood, a more particular description of the invention briefly
described above will be rendered by reference to specific
embodiments that are illustrated in the appended drawings.
Understanding that these drawings depict only typical embodiments
of the invention and are not therefore to be considered to be
limiting of its scope, the invention will be described and
explained with additional specificity and detail through the use of
the accompanying drawings, in which:
FIG. 1 is a block diagram depicting an exemplary cloud computing
node according to an embodiment of the present invention;
FIG. 2 is an additional block diagram depicting an exemplary cloud
computing environment according to an embodiment of the present
invention;
FIG. 3 is an additional block diagram depicting abstraction model
layers according to an embodiment of the present invention;
FIG. 4 is an additional block diagram depicting various user
hardware and cloud computing components functioning in accordance
with aspects of the present invention;
FIG. 5 is a flowchart diagram depicting an exemplary method for
utilizing a human compound eye using an internet of things ("HCEI")
for obstacle protection of a user, in which various aspects of the
present invention may be realized; and
FIG. 6 is an additional flowchart diagram depicting an additional
exemplary method for utilizing a human compound eye using an
internet of things ("HCEI") for obstacle protection of a user,
again in which various aspects of the present invention may be
realized.
DETAILED DESCRIPTION OF THE DRAWINGS
In today's interconnected and complex society, computers and
computer-driven equipment are more commonplace. The "Internet of
Things" refers to the interconnection of uniquely-identifiable
embedded devices within the Internet infrastructure. In an Internet
of Things (`IoT`), a wide variety of devices may exist. Each device
may include different attributes, different capabilities, be
located at different places, and so on. IoT can be thought of as a
dynamic global network infrastructure with self-configuring
capabilities based on standard and interoperable communication
protocols in which both physical and virtual things have identities
and attributes. Such physical and virtual things can be seamlessly
integrated into traditional networks. IoT can make use of
radio-frequency identification (`RFID`) technologies to identify
and inventory each thing in the IoT. IoT can also make use of other
technologies such as barcodes as well.
The advent IoT devices have enabled intelligence across many
devices handled by humans in day-to-day life. For example, in
day-to-day living, when a person is injured there is a need to
guard the wound or injury both when the person is conscious and
performing activities and/or during a period of unconsciousness
such as, for example, when sleeping. For example, a wound or injury
in the feet area may need to be guarded against obstacles such as,
for example, while walking. As an additional example, a person
having recently received cataract surgery may require special care
to protect against rubbing the wound around the eye area or while
asleep.
Similarly, a person with visual challenges may need to be warned or
alerted to approaching obstacles, hazards, and/or dangers, which
may severely injure or cause harm to the person. Accordingly, the
present invention provides a "human compound eye using an internet
of things" ("HCEI") mechanism to identify approaching obstacles and
notifies the individual or others to take necessary action so as to
protect a wounded/injured area or guide the visually challenged
person.
The mechanisms of the illustrated embodiments provide enhancing
mechanisms for utilizing one or more HCEIs for obstacle protection
of a user by a processor. In one embodiment, by way of example
only, a method for protecting a user from one or more objects using
an HCEI device, again by a processor, is provided. One or more
objects may be cognitively determined within an obstacle threshold
distance in relation to a user according to data captured from one
or more internet of things (IoT) devices associated with a wound
dressing, a mobility assistance device, or a combination thereof.
The user may be alerted of the one or more objects within the
obstacle threshold distance.
In an additional aspect, cognitive or "cognition" may refer to a
mental action or process of acquiring knowledge and understanding
through thought, experience, and one or more senses using machine
learning (which may include using sensor based devices or other
computing systems that include audio or video devices). Cognitive
may also refer to identifying patterns of emotions and/or
behaviors, leading to a "learning" of one or more events,
operations, or processes. Thus, the cognitive model may, over time,
develop semantic labels to apply to observed emotions and/or
behaviors and use a knowledge domain or ontology to store the
learned observed emotions and/or behaviors. In one embodiment, the
system provides for progressive levels of complexity in what may be
learned from the one or more events, operations, or processes.
In an additional aspect, the term cognitive may refer to a
cognitive system. The cognitive system may be a specialized
computer system, or set of computer systems, configured with
hardware and/or software logic (in combination with hardware logic
upon which the software executes) to emulate human cognitive
functions. These cognitive systems apply human-like characteristics
to convey and manipulate ideas which, when combined with the
inherent strengths of digital computing, can solve problems with a
high degree of accuracy (e.g., within a defined percentage range or
above an accuracy threshold) and resilience on a large scale. A
cognitive system may perform one or more computer-implemented
cognitive operations that approximate a human thought process while
enabling a user or a computing system to interact in a more natural
manner. A cognitive system may comprise artificial intelligence
logic, such as natural language processing (NLP) based logic, for
example, and machine learning logic, which may be provided as
specialized hardware, software executed on hardware, or any
combination of specialized hardware and software executed on
hardware. The logic of the cognitive system may implement the
cognitive operation(s), examples of which include, but are not
limited to, question answering, identification of related concepts
within different portions of content in a corpus, and intelligent
search algorithms, such as Internet web page searches.
In general, such cognitive systems are able to perform the
following functions: 1) Navigate the complexities of human language
and understanding; 2) Ingest and process vast amounts of structured
and unstructured data; 3) Generate and evaluate hypotheses; 4)
Weigh and evaluate responses that are based only on relevant
evidence; 5) Provide situation-specific advice, insights,
estimations, determinations, evaluations, calculations, and
guidance; 6) Improve knowledge and learn with each iteration and
interaction through machine learning processes; 7) Enable decision
making at the point of impact (contextual guidance); 8) Scale in
proportion to a task, process, or operation; 9) Extend and magnify
human expertise and cognition; 10) Identify resonating, human-like
attributes and traits from natural language; 11) Deduce various
language specific or agnostic attributes from natural language; 12)
Memorize and recall relevant data points (images, text, voice)
(e.g., a high degree of relevant recollection from data points
(images, text, voice) (memorization and recall)); and/or 13)
Predict and sense with situational awareness operations that mimic
human cognition based on experiences.
In an additional aspect, the knowledge domain may be an ontology of
concepts representing a domain of knowledge. A thesaurus or
ontology may be used as the domain knowledge and may also be used
to associate various characteristics, attributes, symptoms,
behaviors, sensitivities, parameters, clinical diagnoses and
treatments of an individual afflicted with autism or person having
any sensory, perceptual, cognitive, emotional/behavioral
challenges, disabilities, or dysfunctions (e.g., neural
dysfunction), and/or any other difficulties in communicating or
engaging in social interaction with other persons. In one aspect,
the term "domain" is a term intended to have its ordinary meaning.
In addition, the term "domain" may include an area of expertise for
a system or a collection of material, information, content and/or
other resources related to a particular subject or subjects.
The term ontology is also a term intended to have its ordinary
meaning. In one aspect, the term ontology in its broadest sense may
include anything that can be modeled as ontology, including but not
limited to, taxonomies, thesauri, vocabularies, and the like. For
example, an ontology may include information or content relevant to
a domain of interest or content of a particular class or concept.
The ontology can be continuously updated with the information
synchronized with the sources, adding information from the sources
to the ontology as models, attributes of models, or associations
between models within the ontology.
Other examples of various aspects of the illustrated embodiments,
and corresponding benefits, will be described further herein.
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud
computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
In cloud computing node 10 there is a computer system/server 12,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context
of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 1, computer system/server 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system/server 12 may include, but are
not limited to, one or more processors or processing units 16, a
system memory 28, and a bus 18 that couples various system
components including system memory 28 to processor 16.
Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 12, and it includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 28 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 30
and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
system memory 28 may include at least one program product having a
set (e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules
42, may be stored in system memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
Referring now to FIG. 2, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 comprises
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 2 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 2) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provides cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provides pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and, in the
context of the illustrated embodiments of the present invention,
various workloads and functions 96 for utilizing one or more HCEI
devices. In addition, workloads and functions 96 for utilizing one
or more HCEI devices may include such operations as data analytics,
data analysis, and as will be further described, notification
functionality. One of ordinary skill in the art will appreciate
that the workloads and functions 96 for utilizing one or more HCEI
devices may also work in conjunction with other portions of the
various abstractions layers, such as those in hardware and software
60, virtualization 70, management 80, and other workloads 90 (such
as data analytics processing 94, for example) to accomplish the
various purposes of the illustrated embodiments of the present
invention.
As previously mentioned, the mechanisms of the illustrated
embodiments provide novel approaches for utilizing a human compound
eye using internet of things ("HCEI") for obstacle protection of a
user by a processor. One or more objects may be determined within
an obstacle threshold distance in relation to a user according to
data captured from one or more internet of things (IoT) devices
associated with a wound dressing, a mobility assistance device, or
a combination thereof. The user may be alerted of the one or more
objects within the obstacle threshold distance.
Turning now to FIG. 4, a block diagram of exemplary functionality
400 relating to utilizing one or more HCEI devices for obstacle
protection of a user. In one aspect, one or more of the components,
modules, services, applications, features and/or functions
described in FIGS. 1-3 may be used in FIG. 4. For example,
functionality 400 may be provided by the computer system/server 12
of FIG. 1.
As shown, the various blocks of functionality are depicted with
arrows designating the blocks' 400 relationships with each other
and to show process flow. Additionally, descriptive information is
also seen relating each of the functional blocks 400. As will be
seen, many of the functional blocks may also be considered
"modules" of functionality, in the same descriptive sense as has
been previously described in FIG. 4. With the foregoing in mind,
the module blocks 400 may also be incorporated into various
hardware and software components of a system for image enhancement
in accordance with the present invention. Many of the functional
blocks 400 may execute as background processes on various
components, either in distributed computing components, or on the
user device, or elsewhere, and generally unaware to the user.
Starting with blocks 402 and 404, one or more HCEI devices such as,
for example, HCEI device 420 may be utilized for user 450 so as to
stream (data 1 from block 402 and data N from block 404) data that
captures and/or identifies one or more objects or obstacles within
an obstacle threshold distance in relation to the user 450. The
HCEI device 420 may capture (in real-time) one or more objects or
obstacles and collect data of a selected and/or defined area within
view of the HCEI device 420. The data may include one or more
obstructing objects that may be stationary and/or moving at one or
more speeds, velocities, and/or distances in relation to the user.
For example, the HCEI device 420 may identify a street sign that is
at least 20 feet from the user 450 and another person walking
towards the user 450 at a determined speed, velocity, and distance.
In one aspect, HCEI device 420 may be an internet of things (IoT)
device such as, for example, one or more types of sensors, media
devices, audio devices, or a combination thereof, associated with a
wound dressing (e.g., a cast, brace, bandage, etc.) or a mobility
assistance device (a cane, a wheelchair, etc., or a combination
thereof). More specifically, the HCEI device 420 may be a camera
such as, for example, a plenoptic burst camera. The HCEI device 420
may also be electric strips (e.g., unimorph piezo electric
strips).
The streamed data (e.g., "streaming data 1" and "streaming data N")
may stream, communicate, and/or send one or more objects or
obstacles (e.g., "obstacle capture" 406a and 406b) via a wireless
communication network and/or computer network to a centralized
computing system such as, for example, a controller/orchestrator,
as in block 408. That is, the HCEI device 420 may stream,
communicate, and/or send the captured obstacles 406a and 406b
(e.g., steps, street signs, humans, animals, objects that may cause
harm to the user, etc.) to the computing system (e.g., a
controller/orchestrator) so as to process, analyze, and/or collect
the captured data.
Moreover, block 408 may also include determining an obstacle
threshold distance for the user 450 by the computing system (e.g.,
a controller/orchestrator). In one aspect, the obstacle threshold
distance may be a preconfigured setting or parameter. The obstacle
threshold distance may also be adjusted, defined, modified, and/or
changed according to one or more contextual factors and/or
environments. For example, the obstacle threshold distance may be a
radius of 10 feet while within an apartment building, but may have
a radius of 100 feet in an outdoor environment. Alternatively, the
obstacle threshold distance may be learned via one or more machine
learning operations according to the various contextual factors.
The computing system (e.g., a controller/orchestrator) may
initialize a machine learning mechanism (which may be included
therein) to learn, set, or modify the obstacle threshold distance
for each of the one or more objects, parameters in the obstacle
threshold distance map, activities of daily living (ADL), movement
patterns of the user, contextual factors, or a combination thereof.
For example, the obstacle threshold distance may be learned
according to one or more machine learning models that learn one or
more patterns, ADL, travel or mobility habits, and/or one or more
preferences. For example, the obstacle threshold distance may be
defined according to the machine learning operations learning that
the user walks to and from work each day taking the same route
while also walking to a nearby park each day after work.
Accordingly, the obstacle threshold distance may be learned (e.g.,
a radius of 5 feet) according to the machine learning and/or in
combination with the user's preferred settings or configurations
for the route to and from work and also the route to and from the
park.
Moreover, blocks 402, 404, and/or 408 may also include the
computing system determining a real-time distance of the one or
more objects from the user. The distance of the one or more objects
from the user may be determined and/or stored in an obstacle
threshold distance map so as to determine the one or more objects
within the obstacle threshold distance. That is, the speed, the
velocity, and/or the distance of the one or more objects (both
stationary and/or in motion) in relation to the user. The
determined speeds, the velocity, and/or the distance(s) may also be
stored in an obstacle threshold distance map.
Moving now to block 410, a communication device such as, for
example, an alarm unit may alert the user of the one or more
objects within an obstacle threshold distance. That is, the
communication device (e.g., the alarm unit) may provide one or more
alerts, suggestions, communications, or a combination thereof to a
graphical user interface ("GUI") of one or more various computing
devices 430 (such as, for example, computing devices 54A-N shown in
FIG. 2) to the user so as to modify mobility and/or a course of
action to avoid the one or more objects within the obstacle
threshold distance. That is, depending upon the speed, velocity,
and/or distance of the captured obstacles to the user 450, the one
or more alerts, suggestions, and/or communications may be provided
to the user 450.
Turning now to block 420, a prediction operation (e.g., a target to
movement predictor) may be performed so as to predict movement of
one or more objects using data obtained from an obstacle threshold
distance map, or data obtained in real-time from one or more IoT
devices associated with the user.
In one aspect, the machine learning operations and modeling for
protecting a user from one or more objects using an HCEI device, as
described herein, may be performed using a wide variety of methods
or combinations of methods, such as supervised learning,
unsupervised learning, temporal difference learning, reinforcement
learning and so forth. Some non-limiting examples of supervised
learning which may be used with the present technology include AODE
(averaged one-dependence estimators), artificial neural network,
backpropagation, Bayesian statistics, naive bays classifier,
Bayesian network, Bayesian knowledge base, case-based reasoning,
decision trees, inductive logic programming, Gaussian process
regression, gene expression programming, group method of data
handling (GMDH), learning automata, learning vector quantization,
minimum message length (decision trees, decision graphs, etc.),
lazy learning, instance-based learning, nearest neighbor algorithm,
analogical modeling, probably approximately correct (PAC) learning,
ripple down rules, a knowledge acquisition methodology, symbolic
machine learning algorithms, sub symbolic machine learning
algorithms, support vector machines, random forests, ensembles of
classifiers, bootstrap aggregating (bagging), boosting
(meta-algorithm), ordinal classification, regression analysis,
information fuzzy networks (IFN), statistical classification,
linear classifiers, fisher's linear discriminant, logistic
regression, perceptron, support vector machines, quadratic
classifiers, k-nearest neighbor, hidden Markov models and boosting.
Some non-limiting examples of unsupervised learning which may be
used with the present technology include artificial neural network,
data clustering, expectation-maximization, self-organizing map,
radial basis function network, vector quantization, generative
topographic map, information bottleneck method, IBSEAD (distributed
autonomous entity systems based interaction), association rule
learning, apriori algorithm, eclat algorithm, FP-growth algorithm,
hierarchical clustering, single-linkage clustering, conceptual
clustering, partitional clustering, k-means algorithm, fuzzy
clustering, and reinforcement learning. Some non-limiting example
of temporal difference learning may include Q-learning and learning
automata. Specific details regarding any of the examples of
supervised, unsupervised, temporal difference or other machine
learning described in this paragraph are known and are within the
scope of this disclosure. Also, when deploying one or more machine
learning models, a computing device may be first tested in a
controlled environment before being deployed in a public setting.
Also even when deployed in a public environment (e.g., external to
the controlled, testing environment), the computing devices may be
monitored for compliance.
In one aspect, the computing system 12/computing environment of
functionality 400 may perform one or more calculations according to
mathematical operations or functions that may involve one or more
mathematical operations (e.g., solving differential equations or
partial differential equations analytically or computationally,
using addition, subtraction, division, multiplication, standard
deviations, means, averages, percentages, statistical modeling
using statistical distributions, by finding minimums, maximums or
similar thresholds for combined variables, etc.).
Turning now to FIG. 5, a method 500 for utilizing a human compound
eye using an internet of things ("HCEI") for obstacle protection of
a user using a processor is depicted, in which various aspects of
the illustrated embodiments may be implemented. The functionality
500 may be implemented as a method executed as instructions on a
machine, where the instructions are included on at least one
computer readable medium or one non-transitory machine-readable
storage medium. In one aspect, the functionality, operations,
and/or architectural designs of FIGS. 1-4 may be implemented all
and/or in part in FIG. 5.
The functionality 500 may start in block 502. One or more objects
may be determined within an obstacle threshold distance in relation
to a user according to data captured from one or more internet of
things (IoT) devices associated with a wound dressing, a mobility
assistance device, or a combination thereof, as in block 504. The
user may be alerted of the one or more objects within the obstacle
threshold distance, as in block 506. The functionality 500 may end,
as in block 508.
Turning now to FIG. 6, an additional method 600 for utilizing an
HCEI device(s) for obstacle protection of a user using a processor
is depicted, in which various aspects of the illustrated
embodiments may be implemented. The functionality 600 may be
implemented as a method executed as instructions on a machine,
where the instructions are included on at least one computer
readable medium or one non-transitory machine-readable storage
medium. In one aspect, the functionality, operations, and/or
architectural designs of FIGS. 1-5 may be implemented all and/or in
part in FIG. 6.
The functionality 600 may start in block 602. Data from one or more
human compound eye using internet of things (HCEI) devices that
identify one or more obstacles in a selected location associated
with a user may be collected, as in block 604. The data relating to
the one or more obstacles in an obstacle threshold distance map may
be stored, as in block 606. Distances may be determined of the one
or more obstacles associated with the obstacle threshold distance
map, as in block 608. One or more communications and/or alerts may
be issued to a user indicating that the one or more obstacles are
within an obstacle threshold distance, as in block 610. The
functionality 600 may end, as in block 612.
In one aspect, in conjunction with and/or as part of at least one
block of FIGS. 5-6, the operations of methods 500 and/or 600 may
include each of the following. The operations of methods 500 and/or
600 may determine a real-time distance of the one or more objects
from the user, and store the distance of the one or more objects
from the user in an obstacle threshold distance map so as to
determine the one or more objects within the obstacle threshold
distance.
A speed, velocity, distance, and/or combination thereof of the one
or more objects may be determined in relation to the user. The
determined speed, the velocity, and/or the distance may be stored
in an obstacle threshold distance map. One or more parameters of
the obstacle threshold distance map may be defined, set,
configured, modified and/or updated by the user and/or a machine
learning operation. The parameters may include obstacle threshold
distance.
The operations of methods 500 and/or 600 may determine one or more
objects via the one or more IoT devices embedded into one or more
locations of the wound dressing, the mobility assistance device, or
a combination thereof. The one or more IoT devices include one or
more types of sensors, media devices, audio devices, or a
combination thereof.
The operations of methods 500 and/or 600 may anticipate and/or
predict movement of one or more objects using data obtained from an
obstacle threshold distance map, or data obtained in real-time from
one or more IoT devices associated with the user. A machine
learning mechanism may be initialized to learn, set, or modify the
obstacle threshold distance for each of the one or more objects,
parameters in the obstacle threshold distance map, activities of
daily living (ADL), movement patterns of the user, or a combination
thereof.
The operations of methods 500 and/or 600 may provide one or more
alerts, suggestions, communications, or a combination thereof via a
graphical user interface to the user so as to modify mobility or a
course of action to avoid the one or more objects within the
obstacle threshold distance. Additionally, one or more alerts,
suggestions, communications, or a combination thereof may also be
provided to an entity (e.g., an alternative user, a caregiver
associated with the user, an associate/family member of the user,
etc.) via a graphical user interface of the entity that may be
providing the communications, so as to assist with modifying the
mobility or a course of action to avoid the one or more objects
within the obstacle threshold distance.
The present invention may be a system, a method, and/or a computer
program product. The computer program product may include a
computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general-purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowcharts and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowcharts and/or
block diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowcharts and/or block diagram block or blocks.
The flowcharts and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowcharts or block diagrams may
represent a module, segment, or portion of instructions, which
comprises one or more executable instructions for implementing the
specified logical function(s). In some alternative implementations,
the functions noted in the block may occur out of the order noted
in the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustrations, and combinations
of blocks in the block diagrams and/or flowchart illustrations, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *